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Aetheris

  • 2 Devlogs
  • 4 Total hours

A real-time platform for tracking, reporting, and improving environmental conditions.

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16m 21s logged

When I first registered for NASA Stardance, Aetheris was already well into development—about 60-70% complete. That’s why my very first official devlog kicked off at Day #6 instead of Day #1. At the time, I just wanted to focus on shipping, but looking back, I want to be transparent about the whole journey.**Even though this devlog covers the timeline from Day 1 to Day 6, the project itself has actually been in the works since March. The idea originally sparked back then under the name “ecomap.” Back then, it only covered air quality districts in Almaty rather than the entire country of Kazakhstan. **However, after that initial phase, it sat on the back burner for a bit. It wasn’t until June that I finally committed to it 100% and went all in.//////////////////////////////////////////////////// Day 1 — FoundationSet up the Next.js 16 project with the App Router and configured it for a fully static build (every route prerendered, no server runtime). Laid the geographic base: Natural Earth land polygons that drive the particle globe. Built the first shell of the landing page./////Day 2 — Map layer systemStarted the map’s layer system. Designed and built the layer legend, which locked in the five environmental layers the whole app is built around: air, industrial, water, biodiversity, and risk./////////Day 3 — Data engine & first surfacesThe biggest push. Built the deterministic simulation engine — a full environmental profile (AQI, PM2.5/PM10, NO₂, temperature, water quality, biodiversity, emissions, sustainability score, 90-day trends) for 28 cities, one per region of Kazakhstan. All values are seeded, so every screen reports the same numbers. On top of that, built the cinematic landing page with a Three.js particle globe and the analytics dashboard with custom SVG charts (temperature anomaly, AQI trends, city rankings). Also scaffolded the map and AI assistant routes and self-hosted the map label fonts so there’s no external CDN dependency — ready to be wired up next.///////////Day 4 — TestingReviewed and tested the build end to end, checked all routes render correctly in the static export, and validated the data across surfaces.////////////Day 5 — Polish & deployFinalized the navigation and the design system (color palette, typography, motion, glass/scanline textures). Centralized all site metadata, SEO, and PWA config into a single source. Fixed a URL-parsing edge case, ran the static production build, and deployed.///////It’s been a wild ride, and I am incredibly happy to say that I have finally finished it!

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Ship #1

I built Aetheris, an environmental operating system for Kazakhstan that visualizes live data like AQI and PM2.5 on an interactive map and explains it using a custom AI analyst. The biggest challenge was rendering massive volumes of sensor data without UI lag and engineering the AI prompts to ground answers in local context, like Almaty's winter temperature inversions, rather than outputting generic facts. I am most proud of this reasoning layer because it transforms raw, disconnected environmental readings into plain-language, actionable insights. To test the project, open the "Atlas" on the live site to explore the different environmental layers, then head to the AI Assistant and ask something specific like, "What is the risk outlook for Almaty?" to see how it interprets the live map data.

  • 2 devlogs
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3h 33m 30s logged

Day 6 Update: Live AQI Map & AI Analyst for Aetheris 🌍🇰🇿

Today I spent my session building out the core infrastructure for Aetheris. My main goal was to bring the Kazakhstan Atlas to life and connect it with our AI reasoning engine.

What I built today:

  • Set up live data fetching for AQI and PM2.5 across 28 cities in Kazakhstan.
  • Implemented the logic for the Environmental AI layer. The assistant now processes real-time sensor data and explains the context (e.g., the heavy smog caused by winter temperature inversions in Almaty).

Challenges faced:
Processing such a massive volume of environmental data points caused severe UI lag during the initial map render. I resolved this by optimizing the data arrays and implementing server-side caching.

Next steps:
In the next session, I will focus on the Community system so citizen scientists can start logging local pollution events and earning contribution points.

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